Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

[1]  Prosun Bhattacharya,et al.  SARS-CoV-2 phase I transmission and mutability linked to the interplay of climatic variables: a global observation on the pandemic spread , 2022, Environmental Science and Pollution Research.

[2]  S. Karuppannan,et al.  Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data , 2021, Environment, Development and Sustainability.

[3]  A. Fekadu,et al.  Containment of COVID-19 in Ethiopia and implications for tuberculosis care and research , 2020, Infectious Diseases of Poverty.

[4]  S. Karuppannan,et al.  Air pollution improvement and mortality rate during COVID-19 pandemic in India: global intersectional study , 2020, Air Quality, Atmosphere & Health.

[5]  H. Pourghasemi,et al.  Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models , 2020, PloS one.

[6]  P. Yin,et al.  Nasopharyngeal Swabs Are More Sensitive Than Oropharyngeal Swabs for COVID-19 Diagnosis and Monitoring the SARS-CoV-2 Load , 2020, Frontiers in Medicine.

[7]  S. Biadgilign,et al.  COVID-19 in Ethiopia: current situation, missed opportunities, and the risk of health system disruptions , 2020, The Pan African medical journal.

[8]  S. Karuppannan,et al.  Application of Geospatial Technologies in the COVID-19 Fight of Ghana , 2020, Transactions of the Indian National Academy of Engineering.

[9]  Mario Coccia,et al.  Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID , 2020, Science of The Total Environment.

[10]  Bagyaraj Murugesan,et al.  Distribution and Trend Analysis of COVID-19 in India: Geospatial Approach , 2020 .

[11]  Y. Liao,et al.  COVID-19: Challenges to GIS with Big Data , 2020, Geography and Sustainability.

[12]  Weizhong Yang,et al.  COVID-19 control in China during mass population movements at New Year , 2020, The Lancet.

[13]  Z. Memish,et al.  The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China , 2020, International Journal of Infectious Diseases.

[14]  K. Baye COVID-19 prevention measures in Ethiopia: Current realities and prospects , 2020 .

[15]  Anh Tuan Tran,et al.  Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers , 2018, Geocarto International.

[16]  Yi Zhou,et al.  Use of evidential reasoning and AHP to assess regional industrial safety , 2018, PloS one.

[17]  F. Ahmad,et al.  Studying Malaria Epidemic for Vulnerability Zones: Multi-Criteria Approach of Geospatial Tools , 2017 .

[18]  C. Y. Ng,et al.  Evidential reasoning-based Fuzzy AHP approach for the evaluation of design alternatives' environmental performances , 2016, Appl. Soft Comput..

[19]  Andrew M Lynn,et al.  Socio-economic, epidemiological and geographic features based on GIS-integrated mapping to identify malarial hotspots , 2015, Malaria Journal.

[20]  Rashmi Sharma,et al.  GIS versus CAD versus DBMS: What Are the Differences? , 2011 .

[21]  Jacek Malczewski,et al.  GIS‐based multicriteria decision analysis: a survey of the literature , 2006, Int. J. Geogr. Inf. Sci..

[22]  J. Martel,et al.  Enhancing Geographical Information Systems Capabilities with Multi-Criteria Evaluation Functions , 2003 .

[23]  Qiming Zhou,et al.  The Application of GIS in the Health Sector: Problems and Prospects ∗ , 1993 .

[24]  Thomas L. Saaty,et al.  Hierarchical analysis of behavior in competition: Prediction in chess , 1980 .

[25]  C. Queen,et al.  DISASTER , 1977, Texas medicine.